DESCRIPTION (Verbatim from the Applicant's Abstract): The overall, long term goal of this work is to develop ways to manipulate cellular responses initiated by ligand/receptor binding; this is done through the combination of mathematical modeling and experiment. The proposed work focuses on developing mathematical models for guanine nucleotide binding protein (G-protein) coupled receptors and G-protein activation. G-proteins are found in virtually every tissue, and they play key roles in, for example, the immune system, vision, brain function, and heart regulation. The activation of G-proteins by receptors at the cell surface initiates a signal transduction pathway that is complex and poorly understood. Receptors can exist in multiple states (active, inactive, ligand-bound, desensitized, internalized, etc.) and these states influence G-protein activation. Further, the kinetics of the transitions between receptor states appear to be important in determining levels and dynamics of G-protein activation and thus a variety of cellular responses. Despite the obvious complexity and dynamics of these signaling processes, most work in the field concentrates on relatively simple equilibrium models of the system. More accurate models of G-protein coupled receptors and G-protein activation are essential to understanding how effective bound ligands are at eliciting cellular responses. Such information is critical to the rational manipulation of cell function for purposes of cell and tissue engineering, and for the development of methods for the development and/or discovery of new phamaceuticals. In this proposal, kinetic models of the G-protein coupled receptor signaling pathway will be developed. Specifically, we will use these models to (1) test the hypothesis that ligand efficacy may be dramatically manipulated by altering cellular parameters, (2) demonstrate the influence that ligand-specific parameters have on signaling and desensitization, and (3) demonstrate conditions under which receptor dimerization may cause larger scale clustering and thus influence signaling. Finally, (4) we will test the hypothesis that common high throughput drug screening assays may be biased against the detection of a class of ligands known as inverse agonists. In each case, models will be used to make predictions that are experimentally accessible and have application to a wide range of G-protein coupled receptor systems.